Privratsky-20210215 - Clustering resolution 0.5 / 23 clusters
Hélène Fradin Kirshner
03/15/2021
source conda/bin/activate privratsky rmarkdown::render(‘./5_Clustering_res_0.5/5_Clustering_res_0.5.Rmd’)
Changes in myeloid and kidney cells after CLP - Analysis of 2 x 10X scRNA-seq samples from 2 pools of WT mice (3 Sham + 3 CLP): comparison of gene expression in different cell populations
rainbow1.7c <- c("#B3ADD3", "#80617D", "#A75769", "#F9C971",
"#A3C587", "#40A8AC", "#296D71")
slices <- rep(1, length(rainbow1.7c))
pie(slices, col = rainbow1.7c)
rainbow2.6c <- c("#03539C", "#12999E", "#B7CE05", "#FAEB09",
"#FDA908", "#E82564")
slices <- rep(1, length(rainbow2.6c))
pie(slices, col = rainbow2.6c)
rainbow3.5c <- c("#f66e6e", "#f6b36e", "#f5f66e", "#6ef3f6",
"#9c6ef6")
slices <- rep(1, length(rainbow3.5c))
pie(slices, col = rainbow3.5c)
rainbow4.12c <- c("#1a1334", "#27294a", "#01545a", "#017352",
"#02c383", "#abd962", "#fbbf46", "#ef6b32", "#ee0445", "#a22b5e",
"#710062", "#022c7d")
slices <- rep(1, length(rainbow4.12c))
pie(slices, col = rainbow4.12c)
indir <- "./processedData/2_1_Resolution_choice"
outdir <- "./processedData/5_Clustering_res_0.5"
dir.create(outdir, recursive = T)library(Seurat)
integrated <- readRDS(paste0(indir, "/8.integrated.rds"))
integrated## An object of class Seurat
## 24399 features across 18055 samples within 2 assays
## Active assay: integrated (2000 features, 2000 variable features)
## 1 other assay present: RNA
## 2 dimensional reductions calculated: pca, umapIdents(integrated) <- "integrated_snn_res.0.5"
table(integrated@active.ident)##
## 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
## 2472 2283 2201 1962 1651 1535 1078 588 519 463 447 435 428 382 344 325
## 16 17 18 19 20 21 22
## 207 185 184 101 89 88 88# pal <- colorRampPalette(c('#12999E', '#FAEB09', '#E82564',
# '#03539C'))
pal <- colorRampPalette(rainbow1.7c)
levels <- levels(integrated$integrated_snn_res.0.5)
colors.clusters <- pal(length(levels))
colors.clusters.2 <- colors.clusters[c(11:14, 1, 7, 2, 20, 3:4,
8, 5, 9, 15, 6, 10, 16, 21, 17:18, 22:23, 19)]
names(colors.clusters.2) <- levels
colors.clusters.2## 0 1 2 3 4 5 6 7
## "#E2A96E" "#F9C971" "#E1C776" "#CAC67D" "#B3ADD3" "#985A70" "#A598BB" "#3B9DA1"
## 8 9 10 11 12 13 14 15
## "#9783A4" "#896E8C" "#A3576A" "#83607B" "#B56B6A" "#B2C583" "#8E5D75" "#CC8A6C"
## 16 17 18 19 20 21 22
## "#99C28A" "#358D91" "#7EBA94" "#63B29E" "#2F7D81" "#296D71" "#48AAA8"slices <- rep(1, length(levels))
pie(slices, col = colors.clusters.2, labels = names(colors.clusters.2))
d <- DimPlot(integrated, reduction = "umap", pt.size = 0.2, label = T,
label.size = 6, cols = colors.clusters.2)
d
pdf(paste0(outdir, "/1_DimPlot_umap_clusters_pc50_res0.5.pdf"),
width = 10, height = 8)
d
dev.off()## png
## 2colors.samples <- c("#12999E", "#FDA908")
names(colors.samples) <- levels(as.factor(integrated$sample.id))
slices <- rep(1, length(colors.samples))
pie(slices, col = colors.samples, labels = names(colors.samples))
p1 <- DimPlot(integrated, reduction = "umap", group.by = "sample.id",
pt.size = 0.2, cols = colors.samples)
p2 <- DimPlot(integrated, reduction = "umap", label = TRUE, pt.size = 0.2,
label.size = 6, cols = colors.clusters.2)
library(cowplot)
plot_grid(p1, p2)
pdf(paste0(outdir, "/2_2DimPlots_umap_samples_clusters_pc50_res0.5.pdf"),
width = 18, height = 8)
plot_grid(p1, p2)
dev.off()## png
## 2d <- DimPlot(integrated, reduction = "umap", group.by = "sample.id",
split.by = "sample.id", pt.size = 0.2, ncol = 2, cols = colors.samples)
d
pdf(paste0(outdir, "/3_DimPlot_umap_split_by_samples.pdf"), width = 16,
height = 9)
d
dev.off()## png
## 2f <- FeaturePlot(integrated, features = c("Nphs2", "Slc5a2",
"Clcnka", "Slc12a1", "Ptgs2", "Slc12a3", "Calb1", "Aqp2",
"Slc4a1", "Slc26a4", "Slc14a2", "Upk1a", "Cd22", "Adgre1",
"Pecam1", "Pdgfrb", "Cd68", "Cd14", "Acta2", "Csf3r", "Cd4"),
min.cutoff = "q9", order = T)
f
pdf(paste0(outdir, "/4_FeaturePlot_cellID.pdf"), width = 28,
height = 42)
f
dev.off()## png
## 2##Annotation of markers based on cluster markers from Susztak Science paper (Park et al., Science 360, 758–763 (2018) and Kidney International (2019) 95, 787–796; https://doi.org/10.1016/
https://science.sciencemag.org/content/360/6390/758.long https://www.kidney-international.org/article/S0085-2538(18)30912-8/fulltext
#Podocyte markers -> cluster 28
f2 <- FeaturePlot(integrated, features = c("Nphs2", "Podxl"),
min.cutoff = "q9")
f2
pdf(paste0(outdir, "/5_FeaturePlot_Podo.pdf"), width = 14, height = 7)
f2
dev.off()## png
## 2#Endothelial markers -> cluster 15
f3 <- FeaturePlot(integrated, features = c("Plat", "Pecam1"),
min.cutoff = "q9")
f3
pdf(paste0(outdir, "/6_FeaturePlot_Endo.pdf"), width = 14, height = 7)
f3
dev.off()## png
## 2#PT-S1 markers -> clusters 7,8,9
f4 <- FeaturePlot(integrated, features = c("Slc5a2", "Slc5a12"),
min.cutoff = "q9")
f4
pdf(paste0(outdir, "/7_FeaturePlot_PTs1.pdf"), width = 14, height = 7)
f4
dev.off()## png
## 2#PT-S2 markers
f5 <- FeaturePlot(integrated, features = c("Fxyd2", "Hrsp12"),
min.cutoff = "q9")
f5
pdf(paste0(outdir, "/8_FeaturePlot_PTs2.pdf"), width = 7, height = 7)
f5
dev.off()## png
## 2#PT-S3 markers -> cluster 5
f6 <- FeaturePlot(integrated, features = c("Atp11a", "Slc13a3"),
min.cutoff = "q9")
f6
pdf(paste0(outdir, "/9_FeaturePlot_PTs3.pdf"), width = 14, height = 7)
f6
dev.off()## png
## 2#Loop of Henle -> clusters 11, 13, 18
f7 <- FeaturePlot(integrated, features = c("Slc12a1", "Umod"),
min.cutoff = "q9")
f7
pdf(paste0(outdir, "/10_FeaturePlot_LOH.pdf"), width = 14, height = 7)
f7
dev.off()## png
## 2#Distal CT -> cluster 10
f8 <- FeaturePlot(integrated, features = c("Slc12a3", "Pvalb"),
min.cutoff = "q9")
f8
pdf(paste0(outdir, "/11_FeaturePlot_DCT.pdf"), width = 14, height = 7)
f8
dev.off()## png
## 2#Conn Tubule -> clusters 6, 20, 21, 29
f21 <- FeaturePlot(integrated, features = c("Calb1"), min.cutoff = "q9")
f21
pdf(paste0(outdir, "/12_FeaturePlot_ConnTub.pdf"), width = 7,
height = 7)
f21
dev.off()## png
## 2#CD PC -> cluster 21
f9 <- FeaturePlot(integrated, features = c("Aqp2", "Hsd11b2"),
min.cutoff = "q9")
f9
pdf(paste0(outdir, "/13_FeaturePlot_CD-PC.pdf"), width = 14,
height = 7)
f9
dev.off()## png
## 2#CD-IC -> clusters 24, 29, 39
f10 <- FeaturePlot(integrated, features = c("Atp6v1g3", "Atp6v0d2"),
min.cutoff = "q9")
f10
pdf(paste0(outdir, "/14_FeaturePlot_CD-IC.pdf"), width = 14,
height = 7)
f10
dev.off()## png
## 2#CD Trans -> cluster 29
f11 <- FeaturePlot(integrated, features = c("Slc26a4", "Insrr",
"Rhbg"), min.cutoff = "q9")
f11
pdf(paste0(outdir, "/15_FeaturePlot_CD-Trans.pdf"), width = 14,
height = 14)
f11
dev.off()## png
## 2#Fibroblast
f12 <- FeaturePlot(integrated, features = c("Plac8", "S100a4",
"Pdgfrb"), min.cutoff = "q9")
f12
pdf(paste0(outdir, "/16_FeaturePlot_Fib.pdf"), width = 14, height = 14)
f12
dev.off()## png
## 2#Macro -> cluster 22
f13 <- FeaturePlot(integrated, features = c("C1qa", "Cd68", "C1qb"),
min.cutoff = "q9")
f13
pdf(paste0(outdir, "/17_FeaturePlot_Macro.pdf"), width = 14,
height = 14)
f13
dev.off()## png
## 2#PMN -> cluster 36
f14 <- FeaturePlot(integrated, features = c("S100a8", "Ly6g",
"S100a9"), min.cutoff = "q9")
f14
pdf(paste0(outdir, "/18_FeaturePlot_PMN.pdf"), width = 14, height = 14)
f14
dev.off()## png
## 2#B lymph -> cluster 37
f15 <- FeaturePlot(integrated, features = c("Cd79a", "Cd79b",
"Cd19"), min.cutoff = "q9")
f15
pdf(paste0(outdir, "/19_FeaturePlot_Blymph.pdf"), width = 14,
height = 14)
f15
dev.off()## png
## 2#Tlymph -> cluster 30
f16 <- FeaturePlot(integrated, features = c("Ltb", "Cd4", "Cxcr6"),
min.cutoff = "q9")
f16
pdf(paste0(outdir, "/20_FeaturePlot_Tlymph.pdf"), width = 14,
height = 14)
f16
dev.off()## png
## 2#NK -> cluster 30
f17 <- FeaturePlot(integrated, features = c("Gzma", "Nkg7"),
min.cutoff = "q9")
f17
pdf(paste0(outdir, "/21_FeaturePlot_NK.pdf"), width = 14, height = 7)
f17
dev.off()## png
## 2#Novel1
f18 <- FeaturePlot(integrated, features = c("Slc27a2", "Lrp2",
"Cdca3"), min.cutoff = "q9")
f18
pdf(paste0(outdir, "/22_FeaturePlot_Novel1.pdf"), width = 14,
height = 14)
f18
dev.off()## png
## 2# library(Seurat)
DefaultAssay(integrated) <- "RNA"
clusters <- levels(integrated@active.ident)
conserved.markers <- data.frame(matrix(ncol = 14))
for (c in clusters) {
print(c)
markers.c <- FindConservedMarkers(integrated, ident.1 = c,
grouping.var = "sample.id", verbose = T, logfc.threshold = -Inf,
min.pct = -Inf, min.cells.feature = -Inf, min.cells.group = -Inf)
markers.c <- cbind(data.frame(cluster = rep(c, dim(markers.c)[1]),
gene = rownames(markers.c)), markers.c)
write.table(markers.c, file = paste0(outdir, "/23_markers_",
c, ".txt"))
colnames(conserved.markers) <- colnames(markers.c)
conserved.markers <- rbind(conserved.markers, markers.c)
head(conserved.markers)
}## [1] "0"
## [1] "1"
## [1] "2"
## [1] "3"
## [1] "4"
## [1] "5"
## [1] "6"
## [1] "7"
## [1] "8"
## [1] "9"
## [1] "10"
## [1] "11"
## [1] "12"
## [1] "13"
## [1] "14"
## [1] "15"
## [1] "16"
## [1] "17"
## [1] "18"
## [1] "19"
## [1] "20"
## [1] "21"
## [1] "22"conserved.markers <- conserved.markers[-1, ]conserved.markers <- conserved.markers[, c(1:2, 13:14, 3:12)]
head(conserved.markers)## cluster gene max_pval minimump_p_val C24_p_val
## D630023F18Rik 0 D630023F18Rik 0 0 0
## Spp2 0 Spp2 0 0 0
## Slc34a3 0 Slc34a3 0 0 0
## Slc5a12 0 Slc5a12 0 0 0
## Car3 0 Car3 0 0 0
## Alpl 0 Alpl 0 0 0
## C24_avg_log2FC C24_pct.1 C24_pct.2 C24_p_val_adj C0_p_val
## D630023F18Rik 0.5152721 0.395 0.020 0 0
## Spp2 2.3501061 0.950 0.176 0 0
## Slc34a3 0.7243040 0.533 0.022 0 0
## Slc5a12 1.0784786 0.788 0.077 0 0
## Car3 0.8505601 0.421 0.029 0 0
## Alpl 1.1738811 0.870 0.155 0 0
## C0_avg_log2FC C0_pct.1 C0_pct.2 C0_p_val_adj
## D630023F18Rik 0.9698330 0.663 0.086 0
## Spp2 2.5368215 0.983 0.339 0
## Slc34a3 0.8490152 0.581 0.044 0
## Slc5a12 1.6953147 0.912 0.190 0
## Car3 0.8773502 0.419 0.065 0
## Alpl 1.0206583 0.817 0.243 0Only top markers that are positive markers
conserved.markers$marker.type <- apply(conserved.markers, 1, function(x) {
y <- as.numeric(x)
if ( (if (is.na(y[6])) {TRUE} else {y[6]>0})
& (if (is.na(y[11])) {TRUE} else {y[11]>0})
# & (if (is.na(y[16])) {TRUE} else {y[16]>0})
# & (if (is.na(y[21])) {TRUE} else {y[21]>0})
# & (if (is.na(y[26])) {TRUE} else {y[26]>0})
# & (if (is.na(y[31])) {TRUE} else {y[31]>0})
# & (if (is.na(y[36])) {TRUE} else {y[36]>0})
# & (if (is.na(y[41])) {TRUE} else {y[41]>0})
)
{"POS"}
else if ( ( if (is.na(y[6])) {TRUE} else {y[6]<0})
& (if (is.na(y[11])) {TRUE} else {y[11]<0})
# & (if (is.na(y[16])) {TRUE} else {y[16]<0})
# & (if (is.na(y[21])) {TRUE} else {y[21]<0})
# & (if (is.na(y[26])) {TRUE} else {y[26]<0})
# & (if (is.na(y[31])) {TRUE} else {y[31]<0})
# & (if (is.na(y[36])) {TRUE} else {y[36]<0})
# & (if (is.na(y[41])) {TRUE} else {y[41]<0})
)
{"NEG"}
else {"UND"}
})
conserved.markers <- conserved.markers[, c(1:4, 15, 5:14)]openxlsx::write.xlsx(conserved.markers, paste0(outdir, "/23_conserved_markers_PC50_res0.5_23clusters.xlsx"),
colNames = T)
head(conserved.markers)## cluster gene max_pval minimump_p_val marker.type
## D630023F18Rik 0 D630023F18Rik 0 0 POS
## Spp2 0 Spp2 0 0 POS
## Slc34a3 0 Slc34a3 0 0 POS
## Slc5a12 0 Slc5a12 0 0 POS
## Car3 0 Car3 0 0 POS
## Alpl 0 Alpl 0 0 POS
## C24_p_val C24_avg_log2FC C24_pct.1 C24_pct.2 C24_p_val_adj
## D630023F18Rik 0 0.5152721 0.395 0.020 0
## Spp2 0 2.3501061 0.950 0.176 0
## Slc34a3 0 0.7243040 0.533 0.022 0
## Slc5a12 0 1.0784786 0.788 0.077 0
## Car3 0 0.8505601 0.421 0.029 0
## Alpl 0 1.1738811 0.870 0.155 0
## C0_p_val C0_avg_log2FC C0_pct.1 C0_pct.2 C0_p_val_adj
## D630023F18Rik 0 0.9698330 0.663 0.086 0
## Spp2 0 2.5368215 0.983 0.339 0
## Slc34a3 0 0.8490152 0.581 0.044 0
## Slc5a12 0 1.6953147 0.912 0.190 0
## Car3 0 0.8773502 0.419 0.065 0
## Alpl 0 1.0206583 0.817 0.243 0library(Seurat)
x <- factor(integrated$integrated_snn_res.0.5)
head(x)## AAACCCAAGATGGCGT--C0 AAACCCAAGCAGTCTT--C0 AAACCCAAGCGAGGAG--C0
## 6 1 3
## AAACCCAAGTAGGGTC--C0 AAACCCAAGTTTGTCG--C0 AAACCCACACTAACGT--C0
## 2 5 5
## Levels: 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22levels(x)## [1] "0" "1" "2" "3" "4" "5" "6" "7" "8" "9" "10" "11" "12" "13" "14"
## [16] "15" "16" "17" "18" "19" "20" "21" "22"library(plyr)
x <- revalue(x, c(`0` = "PCT-S1", `1` = "PST-S3", `2` = "PT-S2",
`3` = "PST-S3", `4` = "LOH", `5` = "Endo", `6` = "Conn Tubule",
`7` = "Macro", `8` = "Distal CT", `9` = "CD-IC", `10` = "Endo",
`11` = "CD-PC", `12` = "Fib", `13` = "PCT-S1", `14` = "CD Trans",
`16` = "Fib", `17` = "NK", `18` = "Podo", `19` = "Fib", `20` = "Fib",
`21` = "B lymph", `22` = "PMN"))
integrated$annotation.1 <- x
head(integrated$annotation.1)## AAACCCAAGATGGCGT--C0 AAACCCAAGCAGTCTT--C0 AAACCCAAGCGAGGAG--C0
## Conn Tubule PST-S3 PST-S3
## AAACCCAAGTAGGGTC--C0 AAACCCAAGTTTGTCG--C0 AAACCCACACTAACGT--C0
## PT-S2 Endo Endo
## 17 Levels: PCT-S1 PST-S3 PT-S2 LOH Endo Conn Tubule Macro Distal CT ... PMNIdents(integrated) <- "annotation.1"library(ggsci)
levels <- levels(integrated$annotation.1)
colors.annotation.1 <- pal_ucscgb("default", alpha = 1)(26)[1:length(levels)]
names(colors.annotation.1) <- levels
colors.annotation.1## PCT-S1 PST-S3 PT-S2 LOH Endo Conn Tubule
## "#FF0000FF" "#FF9900FF" "#FFCC00FF" "#00FF00FF" "#6699FFFF" "#CC33FFFF"
## Macro Distal CT CD-IC CD-PC Fib CD Trans
## "#99991EFF" "#999999FF" "#FF00CCFF" "#CC0000FF" "#FFCCCCFF" "#FFFF00FF"
## 15 NK Podo B lymph PMN
## "#CCFF00FF" "#358000FF" "#0000CCFF" "#99CCFFFF" "#00FFFFFF"slices <- rep(1, length(levels))
pie(slices, col = colors.annotation.1, labels = names(colors.annotation.1))
library(Seurat)
d2 <- DimPlot(integrated, label = TRUE, label.size = 4, group.by = "annotation.1",
cols = colors.annotation.1, repel = T)
d2
pdf(paste0(outdir, "/24_Dimplot_newidents.pdf"), width = 13,
height = 9)
d2
dev.off()## png
## 2d3 <- DimPlot(integrated, group.by = "sample.id", split.by = "sample.id",
pt.size = 0.2, ncol = 2, cols = colors.samples)
d3
pdf(paste0(outdir, "/25_DimPlot_newidents_split_by_samples.pdf"),
width = 16, height = 9)
d3
dev.off()## png
## 2Identify cells expressing Il6
DefaultAssay(integrated) <- "RNA"
f19 <- FeaturePlot(integrated, features = "Il6", order = T, label = T,
label.size = 3)
f19
pdf(paste0(outdir, "/26_FeaturePlot_Il6.pdf"), width = 11, height = 10)
f19
dev.off()## png
## 2f20 <- FeaturePlot(integrated, features = c("Il6"), split.by = "sample.id",
max.cutoff = 3, cols = c("grey", "red"), order = T)
f20
pdf(paste0(outdir, "/27_FeaturePlot_Il6-sham-CLP.pdf"), width = 19,
height = 10)
f20
dev.off()## png
## 2library(ggplot2)
library(cowplot)
theme_set(theme_cowplot())
integrated$celltype.stim <- paste(Idents(integrated), integrated$sample.id,
sep = "_")
integrated$celltype <- Idents(integrated)
Idents(integrated) <- "celltype"plots <- VlnPlot(integrated, features = c("Il6"), split.by = "sample.id",
group.by = "celltype", pt.size = 0, combine = FALSE)
library(patchwork)
wrap_plots(plots = plots, ncol = 1)
d <- DotPlot(integrated, features = "Il6", group.by = "celltype.stim")
openxlsx::write.xlsx(d$data, paste0(outdir, "/28_IL6_expn_per_celltype_stim.xlsx"))d
pdf(paste0(outdir, "/29_DotPlot_IL6_celltype_stim.pdf"), width = 5,
height = 9)
d
dev.off()## png
## 2Idents(integrated) <- "integrated_snn_res.0.5"
cluster19 <- WhichCells(integrated, idents = "19")
# others <- WhichCells(integrated, idents = "33", invert = T)
d <- DimPlot(integrated, label=T, group.by="celltype", cells.highlight= list(cluster19), cols.highlight = c("darkblue"
# , "darkred"
), cols= "grey")
d
pdf(paste0(outdir, "/30_DimPlot_integrated_label_group.by_celltype_cell.highlight_cluster19.pdf"))
d
dev.off()## png
## 2saveRDS(integrated, paste0(outdir, "/31.integrated.rds"))integrated$res.0.5.stim <- paste(integrated$integrated_snn_res.0.5,
integrated$sample.id, sep = "_")d <- DotPlot(integrated, features = "Il6", group.by = "res.0.5.stim")
openxlsx::write.xlsx(d$data, paste0(outdir, "/32_IL6_expn_per_cluster_stim.xlsx"))Idents(integrated) <- "integrated_snn_res.0.5"
f20 <- FeaturePlot(integrated, features = c("Il6"), split.by = "sample.id",
max.cutoff = 3, cols = c("grey", "red"), order = T, label = T)
f20
pdf(paste0(outdir, "/33_FeaturePlot_Il6-sham-CLP_w_labels.pdf"),
width = 19, height = 10)
f20
dev.off()## png
## 2Updated cell types
library(Seurat)
x <- factor(integrated$integrated_snn_res.0.5)
head(x)## AAACCCAAGATGGCGT--C0 AAACCCAAGCAGTCTT--C0 AAACCCAAGCGAGGAG--C0
## 6 1 3
## AAACCCAAGTAGGGTC--C0 AAACCCAAGTTTGTCG--C0 AAACCCACACTAACGT--C0
## 2 5 5
## Levels: 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22levels(x)## [1] "0" "1" "2" "3" "4" "5" "6" "7" "8" "9" "10" "11" "12" "13" "14"
## [16] "15" "16" "17" "18" "19" "20" "21" "22"library(plyr)
x <- revalue(x, c(`0` = "PT-S1", `1` = "PT-S3", `2` = "PT-S2",
`3` = "PT-S3", `4` = "LOH", `5` = "Endo", `6` = "Conn Tubule",
`7` = "Macro", `8` = "Distal Conv T", `9` = "CD-IC", `10` = "Endo",
`11` = "CD-PC", `12` = "Fib", `13` = "PT-S1", `14` = "CD-Trans",
`15` = "CD-IM", `16` = "Fib", `17` = "NK", `18` = "Podo",
`19` = "Fib", `20` = "Fib", `21` = "B lymph", `22` = "PMN"))
integrated$annotation.1 <- x
head(integrated$annotation.1)## AAACCCAAGATGGCGT--C0 AAACCCAAGCAGTCTT--C0 AAACCCAAGCGAGGAG--C0
## Conn Tubule PT-S3 PT-S3
## AAACCCAAGTAGGGTC--C0 AAACCCAAGTTTGTCG--C0 AAACCCACACTAACGT--C0
## PT-S2 Endo Endo
## 17 Levels: PT-S1 PT-S3 PT-S2 LOH Endo Conn Tubule Macro Distal Conv T ... PMNIdents(integrated) <- "annotation.1"cell.types <- c("Endo", "Podo", "PT-S1", "PT-S2", "PT-S3", "LOH",
"Distal Conv T", "Conn Tubule", "CD-PC", "CD-IC", "CD-Trans",
"CD-IM", "Fib", "Macro", "PMN", "B lymph", "NK")integrated$annotation.1 <- factor(integrated$annotation.1, levels = cell.types)library(ggsci)
levels <- levels(integrated$annotation.1)
colors.annotation.1 <- pal_ucscgb("default", alpha = 1)(26)[1:length(levels)]
names(colors.annotation.1) <- levels
colors.annotation.1## Endo Podo PT-S1 PT-S2 PT-S3
## "#FF0000FF" "#FF9900FF" "#FFCC00FF" "#00FF00FF" "#6699FFFF"
## LOH Distal Conv T Conn Tubule CD-PC CD-IC
## "#CC33FFFF" "#99991EFF" "#999999FF" "#FF00CCFF" "#CC0000FF"
## CD-Trans CD-IM Fib Macro PMN
## "#FFCCCCFF" "#FFFF00FF" "#CCFF00FF" "#358000FF" "#0000CCFF"
## B lymph NK
## "#99CCFFFF" "#00FFFFFF"slices <- rep(1, length(levels))
pie(slices, col = colors.annotation.1, labels = names(colors.annotation.1))
library(Seurat)
d2 <- DimPlot(integrated, label = TRUE, label.size = 4, group.by = "annotation.1",
cols = colors.annotation.1, repel = T)
d2
pdf(paste0(outdir, "/34_Dimplot_updated_newidents.pdf"), width = 13,
height = 9)
d2
dev.off()## png
## 2library(Seurat)
Idents(integrated) <- "annotation.1"
d2 <- DimPlot(integrated, label = FALSE, cols = colors.annotation.1) +
NoLegend()
d2
pdf(paste0(outdir, "/35_Dimplot_updated_newidents_NoLegend.pdf"),
width = 7, height = 7)
d2
dev.off()## png
## 2DotPlot with cell markers
Endothelial Plat Endothelial Pecam1 Podocytes Nphs2 Podocytes Podxl PT-S1 Slc5a2 PT-S1 Slc5a12 PT-S2 Fxyd2 PT-S3 Atp11a PT-S3 Slc13a3 LOH Slc12a1 LOH Umod Distal CT Slc12a3 Distal CT Pvalb Conn Tubule Calb1 CD PC Aqp2 CD PC Hsd11b2 CD-IC Atp6v1g3 CD-IC Atp6v0d2 CD Trans Slc26a4 CD Trans Insrr CD Trans Rhbg Inner medullary collecting duct (CD-IM) Slc14a2 Fibroblast Plac8 Fibroblast S100a4 Fibroblast Pdgfrb Macro C1qa Macro Cd68 Macro C1qb PMN S100a8 PMN Ly6g PMN S100a9 B lymph Cd79a B lymph Cd79b B lymph Cd19 NK Gzma NK Nkg7
known.markers <- c("Plat", "Pecam1", "Nphs2", "Podxl", "Slc5a2",
"Slc5a12", "Fxyd2", "Atp11a", "Slc13a3", "Slc12a1", "Umod",
"Slc12a3", "Pvalb", "Calb1", "Aqp2", "Hsd11b2", "Atp6v1g3",
"Atp6v0d2", "Slc26a4", "Insrr", "Rhbg", "Slc14a2", "Plac8",
"S100a4", "Pdgfrb", "C1qa", "Cd68", "C1qb", "S100a8", "Ly6g",
"S100a9", "Cd79a", "Cd79b", "Cd19", "Gzma", "Nkg7")DefaultAssay(integrated) <- "RNA"
d <- DotPlot(object = integrated, features = known.markers, cols = c("#03539C",
"#E82564"), dot.scale = 8, group.by = "annotation.1") + RotatedAxis()
d
pdf(paste0(outdir, "/36_DotPlot_known_markers_cell_types.pdf"),
width = 14, height = 7)
d
dev.off()## png
## 2saveRDS(integrated, paste0(outdir, "/37.annotated.rds"))Session Information
sessionInfo()## R version 4.0.3 (2020-10-10)
## Platform: x86_64-conda-linux-gnu (64-bit)
## Running under: Red Hat Enterprise Linux Server release 6.8 (Santiago)
##
## Matrix products: default
## BLAS: /gpfs/fs1/data/omicscore/Privratsky-Privratsky-20210215/scripts/conda/envs/privratsky/lib/libblas.so.3.8.0
## LAPACK: /gpfs/fs1/data/omicscore/Privratsky-Privratsky-20210215/scripts/conda/envs/privratsky/lib/liblapack.so.3.8.0
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] stats4 parallel stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] data.table_1.13.6 gridExtra_2.3
## [3] forcats_0.5.1 stringr_1.4.0
## [5] purrr_0.3.4 readr_1.4.0
## [7] tidyr_1.1.2 tibble_3.0.6
## [9] tidyverse_1.3.0 SingleR_1.4.0
## [11] celldex_1.0.0 SummarizedExperiment_1.20.0
## [13] Biobase_2.50.0 GenomicRanges_1.42.0
## [15] GenomeInfoDb_1.26.0 IRanges_2.24.0
## [17] S4Vectors_0.28.0 BiocGenerics_0.36.0
## [19] MatrixGenerics_1.2.0 matrixStats_0.58.0
## [21] dplyr_1.0.4 ggsci_2.9
## [23] patchwork_1.1.1 cowplot_1.1.1
## [25] ggplot2_3.3.3 Seurat_4.0.0
## [27] SeuratObject_4.0.0 plyr_1.8.6
##
## loaded via a namespace (and not attached):
## [1] readxl_1.3.1 backports_1.2.1
## [3] AnnotationHub_2.22.0 BiocFileCache_1.14.0
## [5] igraph_1.2.6 lazyeval_0.2.2
## [7] splines_4.0.3 BiocParallel_1.24.0
## [9] listenv_0.8.0 scattermore_0.7
## [11] digest_0.6.27 htmltools_0.5.1.1
## [13] magrittr_2.0.1 memoise_2.0.0
## [15] tensor_1.5 cluster_2.1.1
## [17] ROCR_1.0-11 globals_0.14.0
## [19] modelr_0.1.8 colorspace_2.0-0
## [21] rvest_1.0.0 blob_1.2.1
## [23] rappdirs_0.3.3 ggrepel_0.9.1
## [25] haven_2.3.1 xfun_0.20
## [27] crayon_1.4.1 RCurl_1.98-1.2
## [29] jsonlite_1.7.2 spatstat_1.64-1
## [31] spatstat.data_2.0-0 survival_3.2-7
## [33] zoo_1.8-8 glue_1.4.2
## [35] polyclip_1.10-0 gtable_0.3.0
## [37] zlibbioc_1.36.0 XVector_0.30.0
## [39] leiden_0.3.7 DelayedArray_0.16.0
## [41] BiocSingular_1.6.0 future.apply_1.7.0
## [43] abind_1.4-5 scales_1.1.1
## [45] DBI_1.1.1 miniUI_0.1.1.1
## [47] Rcpp_1.0.6 viridisLite_0.3.0
## [49] xtable_1.8-4 reticulate_1.18
## [51] rsvd_1.0.3 bit_4.0.4
## [53] htmlwidgets_1.5.3 httr_1.4.2
## [55] RColorBrewer_1.1-2 ellipsis_0.3.1
## [57] ica_1.0-2 pkgconfig_2.0.3
## [59] uwot_0.1.10 dbplyr_2.1.0
## [61] deldir_0.2-9 tidyselect_1.1.0
## [63] rlang_0.4.10 reshape2_1.4.4
## [65] later_1.1.0.1 AnnotationDbi_1.52.0
## [67] cellranger_1.1.0 munsell_0.5.0
## [69] BiocVersion_3.12.0 tools_4.0.3
## [71] cachem_1.0.4 cli_2.3.0
## [73] ExperimentHub_1.16.0 generics_0.1.0
## [75] RSQLite_2.2.4 broom_0.7.5
## [77] ggridges_0.5.3 evaluate_0.14
## [79] fastmap_1.1.0 yaml_2.2.1
## [81] goftest_1.2-2 fs_1.5.0
## [83] knitr_1.31 bit64_4.0.5
## [85] fitdistrplus_1.1-3 RANN_2.6.1
## [87] pbapply_1.4-3 future_1.21.0
## [89] nlme_3.1-152 sparseMatrixStats_1.2.0
## [91] mime_0.10 formatR_1.7
## [93] xml2_1.3.2 rstudioapi_0.13
## [95] compiler_4.0.3 plotly_4.9.3
## [97] curl_4.3 png_0.1-7
## [99] interactiveDisplayBase_1.28.0 spatstat.utils_2.0-0
## [101] reprex_1.0.0 stringi_1.5.3
## [103] ps_1.5.0 lattice_0.20-41
## [105] Matrix_1.3-2 vctrs_0.3.6
## [107] pillar_1.4.7 lifecycle_1.0.0
## [109] BiocManager_1.30.10 lmtest_0.9-38
## [111] BiocNeighbors_1.8.0 RcppAnnoy_0.0.18
## [113] bitops_1.0-6 irlba_2.3.3
## [115] httpuv_1.5.5 R6_2.5.0
## [117] promises_1.2.0.1 KernSmooth_2.23-18
## [119] parallelly_1.23.0 codetools_0.2-18
## [121] MASS_7.3-53.1 assertthat_0.2.1
## [123] withr_2.4.1 sctransform_0.3.2
## [125] GenomeInfoDbData_1.2.4 hms_1.0.0
## [127] mgcv_1.8-33 beachmat_2.6.0
## [129] grid_4.0.3 rpart_4.1-15
## [131] rmarkdown_2.6 DelayedMatrixStats_1.12.0
## [133] Rtsne_0.15 lubridate_1.7.10
## [135] shiny_1.6.0writeLines(capture.output(sessionInfo()), "./scripts/5_Clustering_res_0.5/5_Clustering_res_0.5.sessionInfo.txt")